Many search users may search a content corpus (e.g., social media networks, digital image sharing websites, public wide area networks, etc.) to locate relevant content items (e.g., digital images, videos, audio recordings, etc.) on a regular basis. Unfortunately, search results for certain types of content items may have limited accuracy. In an example, a search user may submit a search query through a search engine to locate digital images associated with an event, such as Halloween. The search engine may utilize the search query to generate search results for content items within the content corpus that correspond to the search query (e.g., images explicitly tagged as Halloween and/or photos captured on October 31st). However, search users often over specify search queries, such as in regards to date ranges and/or date-based terms (e.g., a search user may personally associate Halloween with a broader date range, such as 2 weeks leading up to Halloween, than how a search query of “Halloween” is interpreted such as corresponding to merely October 31st). Thus, a search engine may omit relevant content items from search results (e.g., a digital image created on October 16th may be interesting to the search user, but the digital image may be omitted from search results because the search query of “Halloween” may be limited to October 31st). Unfortunately, many computing devices and/or search engines may lack technology that can accurately identify relevant content items because such content items may not correspond to exact search query dates. Because relevant content items may end up being omitted from search results, search users may need to submit multiple search queries to locate desired content items.